Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat-map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.Comment: This version has been submitted to CAAI Transactions on Intelligence Technology. 202

Mazabraud's Syndrome Coexisting with a Uterine Tumor Resembling an Ovarian Sex Cord Tumor (UTROSCT): a Case Report

The association of intramuscular myxoma and fibrous dysplasia is a rare disease known as Mazabraud's syndrome. We present a case of Mazabraud's syndrome coexisting with a uterine tumor and resembling an ovarian sex cord tumor (UTROSCT). This uterine tumor showed a high mitotic index and cytological atypia. To the best of our knowledge, the coexistence of the two different entities has not been reported in the literature

Evaluation of labral pathology and hip articular cartilage in patients with femoroacetabular impingement (FAI) : comparison of multidetector CT arthrography and MR arthrography

Background: To compare the multidetector computed tomography (MDCT) arthrography (CTa) and magnetic resonance (MR) arthrography (MRa) findings with surgical findings in patients with femoroacetabular impingement (FAI) and to evaluate the diagnostic performance of these methods. Material/Methods: Labral pathology and articular cartilage were prospectively evaluated with MRa and CTa in 14 hips of 14 patients. The findings were evaluated by two musculoskeletal radiologists with 10 and 20 years of experience, respectively. Sensitivity, specificity, accuracy, and positive predictive value were determined using surgical findings as the standard of reference. Results: While the disagreement between observers was recorded in two cases of labral tearing with MRa, there was a complete consensus with CTa. Disagreement between observers was found in four cases of femoral cartilage loss with both MRa and CTa. Disagreement was also recorded in only one case of acetabular cartilage loss with both methods. The percent sensitivity, specificity, and accuracy for correctly assessing the labral tearing were as follows for MRa/CTa, respectively: 100/100, 50/100, 86/100 (p0.05) and for femoral cartilage assessment were 100/75, 90/70, 86/71 (p>0.05). Inter-observer reliability value showed excellent agreement for labral tearing with CTa (k=1.0). Inter-observer agreement was substantial to excellent with regard to acetabular cartilage assessment with MRa and CTa (k=0.76 for MRa and k=0.86 for CTa) Conclusions: Inter-observer reliability with CTa is excellent for labral tearing assessment. CTa seems to have an equal sensitivity and a higher specificity than MRa for the detection of labral pathology. MRa is better, but not statistically significantly, in demonstrating acetabular and femoral cartilage pathology